Visual Analysis of Spatio-Temporal Data: Applications in Weather Forecasting
Weather conditions affect multiple aspects of human life such as economy, safety, security, and social activities. For this reason, weather forecast plays a major role in society. Currently weather forecasts are based on Numerical Weather Prediction (NWP) models that generate a representation of the...
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| Published in | Computer graphics forum Vol. 34; no. 3; pp. 381 - 390 |
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| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Blackwell Publishing Ltd
01.06.2015
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Weather conditions affect multiple aspects of human life such as economy, safety, security, and social activities. For this reason, weather forecast plays a major role in society. Currently weather forecasts are based on Numerical Weather Prediction (NWP) models that generate a representation of the atmospheric flow. Interactive visualization of geo‐spatial data has been widely used in order to facilitate the analysis of NWP models. This paper presents a visualization system for the analysis of spatio‐temporal patterns in short‐term weather forecasts. For this purpose, we provide an interactive visualization interface that guides users from simple visual overviews to more advanced visualization techniques. Our solution presents multiple views that include a timeline with geo‐referenced maps, an integrated webmap view, a forecast operation tool, a curve‐pattern selector, spatial filters, and a linked meteogram. Two key contributions of this work are the timeline with geo‐referenced maps and the curve‐pattern selector. The latter provides novel functionality that allows users to specify and search for meaningful patterns in the data. The visual interface of our solution allows users to detect both possible weather trends and errors in the weather forecast model. We illustrate the usage of our solution with a series of case studies that were designed and validated in collaboration with domain experts. |
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| AbstractList | Weather conditions affect multiple aspects of human life such as economy, safety, security, and social activities. For this reason, weather forecast plays a major role in society. Currently weather forecasts are based on Numerical Weather Prediction (NWP) models that generate a representation of the atmospheric flow. Interactive visualization of geo-spatial data has been widely used in order to facilitate the analysis of NWP models. This paper presents a visualization system for the analysis of spatio-temporal patterns in short-term weather forecasts. For this purpose, we provide an interactive visualization interface that guides users from simple visual overviews to more advanced visualization techniques. Our solution presents multiple views that include a timeline with geo-referenced maps, an integrated webmap view, a forecast operation tool, a curve-pattern selector, spatial filters, and a linked meteogram. Two key contributions of this work are the timeline with geo-referenced maps and the curve-pattern selector. The latter provides novel functionality that allows users to specify and search for meaningful patterns in the data. The visual interface of our solution allows users to detect both possible weather trends and errors in the weather forecast model. We illustrate the usage of our solution with a series of case studies that were designed and validated in collaboration with domain experts. |
| Author | Gröller, M. E. Pelorosso, L. Delrieux, C. Bruckner, S. Diehl, A. Saulo, C. Ruiz, J. |
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| CitedBy_id | crossref_primary_10_1109_TBDATA_2018_2877352 crossref_primary_10_1109_TVCG_2017_2744418 crossref_primary_10_1111_cgf_12929 crossref_primary_10_1016_j_rser_2017_09_078 crossref_primary_10_1016_j_visinf_2019_08_001 crossref_primary_10_1111_cgf_13210 crossref_primary_10_1109_TVCG_2023_3326514 crossref_primary_10_1109_TPWRS_2018_2822784 crossref_primary_10_1109_TVCG_2017_2779501 crossref_primary_10_1111_cgf_12899 crossref_primary_10_1111_cgf_13279 crossref_primary_10_1111_cgf_13731 crossref_primary_10_1016_j_cag_2022_01_007 crossref_primary_10_1111_cgf_14308 crossref_primary_10_1007_s10462_021_09994_y crossref_primary_10_1109_TVCG_2017_2674918 crossref_primary_10_1007_s41060_017_0072_z crossref_primary_10_1109_JBHI_2023_3234818 crossref_primary_10_1109_MCG_2019_2926242 |
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| References_xml | – reference: Andrienko N.V., Andrienko G.L.: Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach, 2006 ed. Springer, 2006. 2 – reference: Rozumalski R.: WRF Environmental Modeling System - User's Guide, release 2.1.2.2 ed. National Weather Service SOO Science and Training Resource Center, May 2006. 3 – reference: Song Y., Ye J., Svakhine N., Lasher-Trapp S., Baldwin M., Ebert D.: An atmospheric visual analysis and exploration system. IEEE Transactions on Visualization and Computer Graphics 12, 5 (September 2006), 1157-1164. doi:10.1109/TVCG.2006.117. 3 – reference: Malik M.M., Heinzl C., Gröller M. E.: Comparative visualization for parameter studies of dataset series. IEEE Transaction on Visualization and Computer Graphics 16, 5 (September 2010), 829-840. 2 – reference: Krstajic M., Bertini E., Keim D.A.: CloudLines: Compact Display of Event Episodes in Multiple Time-Series. IEEE Transactions on Visualization and Computer Graphics 17 (2011), 2432-2439. 2 – reference: Hochheiser H., Shneiderman B.: Dynamic query tools for time series data sets: timebox widgets for interactive exploration. Information Visualization 3, 1 (March 2004), 1-18. doi:10.1145/993176.993177. 2 – reference: Coto E., Grimm S., Bruckner S., Gröller M. E., Kanitsar A., Rodriguez O.: Mammoexplorer: An advanced CAD application for breast DCE-MRI. In Proceedings of Vision, Modelling, and Visualization 2005 (November 2005), pp. 91-98. 3 – reference: Ware C., Plumlee M.D.: Designing a better weather display. Information Visualization 12, 3-4 (2013), 221-239. 2 – reference: Ruiz J.J., Saulo C., Nogués-Paegle J.: WRF model sensitivity to choice of parameterization over south america: Validation against surface variables. Monthly Weather Review 138, 8 (August 2010), 3342-3355. 3 – reference: Clyne J., Mininni P., Norton A., Rast M.: Interactive desktop analysis of high resolution simulations: application to turbulent plume dynamics and current sheet formation. New Journal of Physics 9, 8 (2007), 301. 3 – reference: Schmidt J., Gröller M. E., Bruckner S.: VAICo: Visual analysis for image comparison. IEEE Transactions on Visualization and Computer Graphics 19, 12 (December 2013), 2090-2099. 2 – reference: Glatter M., Huang J., Ahern S., Daniel J., Lu A.: Visualizing temporal patterns in large multivariate data using modified globbing. IEEE Transactions on Visualization and Computer Graphics 14, 6 (2008), 1467-1474. doi:http://doi.ieeecomputersociety.org/10.1109/TVCG.2008.184. 3 – reference: Woodring J., Shen H.: Multiscale time activity data exploration via temporal clustering visualization spreadsheet. IEEE Transactions on Visualization and Computer Graphics 15, 1 (2009), 123-137. doi:10.1109/TVCG.2008.69. 3 – reference: Bruckner S., Möller T.: Result-driven exploration of simulation parameter spaces for visual effects design. IEEE Transactions on Visualization and Computer Graphics 16, 6 (October 2010), 1467-1475. 2 – reference: Buono P., Aris A., Plaisant C., Khella A., Shneiderman B.: Interactive pattern search in time series. Proceedings of SPIE 5669, 2 (2005), 175-186. doi:10.1117/12.587537. 2 – reference: Jolliffe I.T., Stephenson D.B.: Forecast Verification: A Practitioner's Guide in Atmospheric Science, 2nd Edition. John Wiley and Sons Ltd, 2011. 6 – reference: Aigner W., Miksch S., Schumann H., Tominski C.: Visualization of Time-Oriented Data. Springer, London, UK, 2011. doi:10.1007/978-0-85729-079-3. 2 – reference: Williams D., Doutriaux C., Patchett J., Williams S., Shipman G., Miller R., Steed C., Krishnan H., Silva C., Chaudhary A., Bremer P.-T., Pugmire D., Bethel W., Childs H., Prabhat M., Geveci B., Bauer A., Pletzer A., Poco J., Ellqvist T., Santos E., Potter G., Smith B., Maxwell T., Kindig D., Koop D.: The Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT): Data Analysis and Visualization for Geoscience Data. IEEE Computer 99, PrePrints (2013), 1. LBNL-6278E. doi:http://doi.ieeecomputersociety.org/10.1109/MC.2013.119. 3 – reference: Kehrer J., Ladstädter F., Muigg P., Doleisch H., Steiner A., Hauser H.: Hypothesis generation in climate research with interactive visual data exploration. 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| SubjectTerms | Analysis Categories and Subject Descriptors (according to ACM CCS) Climatology Computer graphics Economic conditions I.3.3 [Computer Graphics]: Picture/Image Generation-Viewing algorithms I.3.6 [Computer Graphics]: Methodology and Techniques Interaction techniques I.3.8 [Computer Graphics]: Applications-Weather Forecast Analysis Interactive Mathematical models Selectors Social conditions & trends Studies Visual Visualization Weather Weather forecasting |
| Title | Visual Analysis of Spatio-Temporal Data: Applications in Weather Forecasting |
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